Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations891
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.2 KiB
Average record size in memory107.1 B

Variable types

Categorical7
Numeric7
Boolean1

Alerts

Age is highly overall correlated with Age*ClassHigh correlation
Age*Class is highly overall correlated with Age and 3 other fieldsHigh correlation
Age*Fare is highly overall correlated with FareHigh correlation
Alone is highly overall correlated with FamilySize and 2 other fieldsHigh correlation
Cabin is highly overall correlated with PclassHigh correlation
FamilySize is highly overall correlated with Alone and 3 other fieldsHigh correlation
Fare is highly overall correlated with Age*Class and 2 other fieldsHigh correlation
Parch is highly overall correlated with Alone and 1 other fieldsHigh correlation
Pclass is highly overall correlated with Age*Class and 1 other fieldsHigh correlation
Sex is highly overall correlated with Survived and 1 other fieldsHigh correlation
SibSp is highly overall correlated with Alone and 1 other fieldsHigh correlation
Survived is highly overall correlated with Sex and 1 other fieldsHigh correlation
Title is highly overall correlated with Age*Class and 2 other fieldsHigh correlation
Cabin is highly imbalanced (57.2%) Imbalance
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
SibSp has 608 (68.2%) zeros Zeros
Parch has 678 (76.1%) zeros Zeros
Fare has 15 (1.7%) zeros Zeros
Age*Fare has 15 (1.7%) zeros Zeros

Reproduction

Analysis started2025-09-08 16:46:44.293150
Analysis finished2025-09-08 16:46:53.330554
Duration9.04 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
3
491 
1
216 
2
184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Length

2025-09-08T16:46:53.736492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-08T16:46:53.886233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring characters

ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 491
55.1%
1 216
24.2%
2 184
 
20.7%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
male
577 
female
314 

Length

Max length6
Median length4
Mean length4.704826
Min length4

Characters and Unicode

Total characters4192
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Length

2025-09-08T16:46:54.071130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-08T16:46:54.229856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
male 577
64.8%
female 314
35.2%

Most occurring characters

ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1205
28.7%
m 891
21.3%
a 891
21.3%
l 891
21.3%
f 314
 
7.5%

Age
Categorical

High correlation 

Distinct15
Distinct (%)1.7%
Missing1
Missing (%)0.1%
Memory size1.8 KiB
[30, 35)
215 
[20, 25)
150 
[25, 30)
106 
[35, 40)
89 
[15, 20)
86 
Other values (10)
244 

Length

Max length8
Median length8
Mean length7.8764045
Min length6

Characters and Unicode

Total characters7010
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[20, 25)
2nd row[35, 40)
3rd row[25, 30)
4th row[35, 40)
5th row[35, 40)

Common Values

ValueCountFrequency (%)
[30, 35) 215
24.1%
[20, 25) 150
16.8%
[25, 30) 106
11.9%
[35, 40) 89
10.0%
[15, 20) 86
 
9.7%
[40, 45) 48
 
5.4%
[0, 5) 44
 
4.9%
[45, 50) 41
 
4.6%
[50, 55) 32
 
3.6%
[5, 10) 22
 
2.5%
Other values (5) 57
 
6.4%

Length

2025-09-08T16:46:54.407500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
30 321
18.0%
35 304
17.1%
25 256
14.4%
20 236
13.3%
40 137
7.7%
15 102
 
5.7%
45 89
 
5.0%
50 73
 
4.1%
5 66
 
3.7%
55 48
 
2.7%
Other values (6) 148
8.3%

Most occurring characters

ValueCountFrequency (%)
5 1011
14.4%
[ 890
12.7%
0 890
12.7%
, 890
12.7%
890
12.7%
) 890
12.7%
3 625
8.9%
2 492
7.0%
4 226
 
3.2%
1 140
 
2.0%
Other values (2) 66
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 1011
14.4%
[ 890
12.7%
0 890
12.7%
, 890
12.7%
890
12.7%
) 890
12.7%
3 625
8.9%
2 492
7.0%
4 226
 
3.2%
1 140
 
2.0%
Other values (2) 66
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 1011
14.4%
[ 890
12.7%
0 890
12.7%
, 890
12.7%
890
12.7%
) 890
12.7%
3 625
8.9%
2 492
7.0%
4 226
 
3.2%
1 140
 
2.0%
Other values (2) 66
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 1011
14.4%
[ 890
12.7%
0 890
12.7%
, 890
12.7%
890
12.7%
) 890
12.7%
3 625
8.9%
2 492
7.0%
4 226
 
3.2%
1 140
 
2.0%
Other values (2) 66
 
0.9%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52300786
Minimum0
Maximum8
Zeros608
Zeros (%)68.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:54.563443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1027434
Coefficient of variation (CV)2.1084644
Kurtosis17.88042
Mean0.52300786
Median Absolute Deviation (MAD)0
Skewness3.6953517
Sum466
Variance1.2160431
MonotonicityNot monotonic
2025-09-08T16:46:54.723358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
4 18
 
2.0%
3 16
 
1.8%
8 7
 
0.8%
5 5
 
0.6%
ValueCountFrequency (%)
0 608
68.2%
1 209
 
23.5%
2 28
 
3.1%
3 16
 
1.8%
4 18
 
2.0%
5 5
 
0.6%
8 7
 
0.8%
ValueCountFrequency (%)
8 7
 
0.8%
5 5
 
0.6%
4 18
 
2.0%
3 16
 
1.8%
2 28
 
3.1%
1 209
 
23.5%
0 608
68.2%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38159371
Minimum0
Maximum6
Zeros678
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:54.873096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80605722
Coefficient of variation (CV)2.1123441
Kurtosis9.7781252
Mean0.38159371
Median Absolute Deviation (MAD)0
Skewness2.749117
Sum340
Variance0.64972824
MonotonicityNot monotonic
2025-09-08T16:46:55.024020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
5 5
 
0.6%
3 5
 
0.6%
4 4
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 678
76.1%
1 118
 
13.2%
2 80
 
9.0%
3 5
 
0.6%
4 4
 
0.4%
5 5
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.6%
4 4
 
0.4%
3 5
 
0.6%
2 80
 
9.0%
1 118
 
13.2%
0 678
76.1%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct248
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.204208
Minimum0
Maximum512.3292
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:55.219907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.9104
median14.4542
Q331
95-th percentile112.07915
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.0896

Descriptive statistics

Standard deviation49.693429
Coefficient of variation (CV)1.5430725
Kurtosis33.398141
Mean32.204208
Median Absolute Deviation (MAD)6.9042
Skewness4.7873165
Sum28693.949
Variance2469.4368
MonotonicityNot monotonic
2025-09-08T16:46:55.430863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 43
 
4.8%
13 42
 
4.7%
7.8958 38
 
4.3%
7.75 34
 
3.8%
26 31
 
3.5%
10.5 24
 
2.7%
7.925 18
 
2.0%
7.775 16
 
1.8%
7.2292 15
 
1.7%
0 15
 
1.7%
Other values (238) 615
69.0%
ValueCountFrequency (%)
0 15
1.7%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.2%
6.75 2
 
0.2%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.3%
263 4
0.4%
262.375 2
0.2%
247.5208 2
0.2%
227.525 4
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 3
0.3%
164.8667 2
0.2%
153.4625 3
0.3%

Cabin
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
missing
687 
C
 
59
B
 
47
D
 
33
E
 
32
Other values (4)
 
33

Length

Max length7
Median length7
Mean length5.6262626
Min length1

Characters and Unicode

Total characters5013
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowmissing
2nd rowC
3rd rowmissing
4th rowC
5th rowmissing

Common Values

ValueCountFrequency (%)
missing 687
77.1%
C 59
 
6.6%
B 47
 
5.3%
D 33
 
3.7%
E 32
 
3.6%
A 15
 
1.7%
F 13
 
1.5%
G 4
 
0.4%
T 1
 
0.1%

Length

2025-09-08T16:46:55.631143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-08T16:46:55.818944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
missing 687
77.1%
c 59
 
6.6%
b 47
 
5.3%
d 33
 
3.7%
e 32
 
3.6%
a 15
 
1.7%
f 13
 
1.5%
g 4
 
0.4%
t 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 1374
27.4%
s 1374
27.4%
m 687
13.7%
n 687
13.7%
g 687
13.7%
C 59
 
1.2%
B 47
 
0.9%
D 33
 
0.7%
E 32
 
0.6%
A 15
 
0.3%
Other values (3) 18
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5013
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1374
27.4%
s 1374
27.4%
m 687
13.7%
n 687
13.7%
g 687
13.7%
C 59
 
1.2%
B 47
 
0.9%
D 33
 
0.7%
E 32
 
0.6%
A 15
 
0.3%
Other values (3) 18
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5013
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1374
27.4%
s 1374
27.4%
m 687
13.7%
n 687
13.7%
g 687
13.7%
C 59
 
1.2%
B 47
 
0.9%
D 33
 
0.7%
E 32
 
0.6%
A 15
 
0.3%
Other values (3) 18
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5013
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1374
27.4%
s 1374
27.4%
m 687
13.7%
n 687
13.7%
g 687
13.7%
C 59
 
1.2%
B 47
 
0.9%
D 33
 
0.7%
E 32
 
0.6%
A 15
 
0.3%
Other values (3) 18
 
0.4%

Embarked
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
S
646 
C
168 
Q
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Length

2025-09-08T16:46:56.004193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-08T16:46:56.157204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 646
72.5%
c 168
 
18.9%
q 77
 
8.6%

Most occurring characters

ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 646
72.5%
C 168
 
18.9%
Q 77
 
8.6%

Title
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Mr
537 
Miss
185 
Mrs
129 
Master
 
40

Length

Max length6
Median length2
Mean length2.7396184
Min length2

Characters and Unicode

Total characters2441
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 537
60.3%
Miss 185
 
20.8%
Mrs 129
 
14.5%
Master 40
 
4.5%

Length

2025-09-08T16:46:56.341452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-08T16:46:56.507689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mr 537
60.3%
miss 185
 
20.8%
mrs 129
 
14.5%
master 40
 
4.5%

Most occurring characters

ValueCountFrequency (%)
M 891
36.5%
r 706
28.9%
s 539
22.1%
i 185
 
7.6%
a 40
 
1.6%
t 40
 
1.6%
e 40
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2441
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 891
36.5%
r 706
28.9%
s 539
22.1%
i 185
 
7.6%
a 40
 
1.6%
t 40
 
1.6%
e 40
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2441
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 891
36.5%
r 706
28.9%
s 539
22.1%
i 185
 
7.6%
a 40
 
1.6%
t 40
 
1.6%
e 40
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2441
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 891
36.5%
r 706
28.9%
s 539
22.1%
i 185
 
7.6%
a 40
 
1.6%
t 40
 
1.6%
e 40
 
1.6%

Age*Class
Real number (ℝ)

High correlation 

Distinct137
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.098257
Minimum0.92
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:56.696395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.92
5-th percentile13.7225
Q139.5
median63
Q391.75
95-th percentile120
Maximum222
Range221.08
Interquartile range (IQR)52.25

Descriptive statistics

Standard deviation33.709901
Coefficient of variation (CV)0.51783108
Kurtosis0.75791423
Mean65.098257
Median Absolute Deviation (MAD)25
Skewness0.55697425
Sum58002.547
Variance1136.3574
MonotonicityNot monotonic
2025-09-08T16:46:56.894928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.95323741 90
 
10.1%
65.53691275 33
 
3.7%
48 27
 
3.0%
60 26
 
2.9%
54 26
 
2.9%
66 23
 
2.6%
32.98441247 21
 
2.4%
72 20
 
2.2%
63 18
 
2.0%
78 17
 
1.9%
Other values (127) 590
66.2%
ValueCountFrequency (%)
0.92 1
 
0.1%
1.26 1
 
0.1%
1.34 1
 
0.1%
1.66 2
 
0.2%
2 3
 
0.3%
2.25 2
 
0.2%
3 5
0.6%
4 3
 
0.3%
6 10
1.1%
8 2
 
0.2%
ValueCountFrequency (%)
222 1
 
0.1%
211.5 1
 
0.1%
195 1
 
0.1%
189 1
 
0.1%
183 1
 
0.1%
177 1
 
0.1%
166.5 1
 
0.1%
153 3
0.3%
150 1
 
0.1%
147 1
 
0.1%

Age*Fare
Real number (ℝ)

High correlation  Zeros 

Distinct706
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1019.5328
Minimum0
Maximum18443.851
Zeros15
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:57.089899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.293415
Q1207
median354.2
Q31018.2584
95-th percentile4273.2009
Maximum18443.851
Range18443.851
Interquartile range (IQR)811.25839

Descriptive statistics

Standard deviation1814.5111
Coefficient of variation (CV)1.7797476
Kurtosis35.971287
Mean1019.5328
Median Absolute Deviation (MAD)202.1363
Skewness5.0243704
Sum908403.71
Variance3292450.4
MonotonicityNot monotonic
2025-09-08T16:46:57.296278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
1.7%
260.438324 15
 
1.7%
265.5245204 12
 
1.3%
255.6291966 11
 
1.2%
169.3036913 9
 
1.0%
238.4509146 6
 
0.7%
238.3123801 5
 
0.6%
442 4
 
0.4%
390 4
 
0.4%
159.5 4
 
0.4%
Other values (696) 806
90.5%
ValueCountFrequency (%)
0 15
1.7%
3.577014 1
 
0.1%
9.715 1
 
0.1%
11.1333 1
 
0.1%
14.443725 2
 
0.2%
15.5625 1
 
0.1%
15.7417 1
 
0.1%
20.575 1
 
0.1%
20.925 1
 
0.1%
24.07 1
 
0.1%
ValueCountFrequency (%)
18443.8512 1
0.1%
17931.522 2
0.2%
16832 1
0.1%
12376.04 1
0.1%
9556.05 1
0.1%
9087.5125 1
0.1%
8900.825 1
0.1%
8645.95 1
0.1%
8498.2064 1
0.1%
7504.778447 1
0.1%

Alone
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1023.0 B
False
537 
True
354 
ValueCountFrequency (%)
False 537
60.3%
True 354
39.7%
2025-09-08T16:46:57.467989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

FamilySize
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9046016
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:57.602611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6134585
Coefficient of variation (CV)0.84713704
Kurtosis9.159666
Mean1.9046016
Median Absolute Deviation (MAD)0
Skewness2.7274415
Sum1697
Variance2.6032485
MonotonicityNot monotonic
2025-09-08T16:46:57.758346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
6 22
 
2.5%
5 15
 
1.7%
7 12
 
1.3%
11 7
 
0.8%
8 6
 
0.7%
ValueCountFrequency (%)
1 537
60.3%
2 161
 
18.1%
3 102
 
11.4%
4 29
 
3.3%
5 15
 
1.7%
6 22
 
2.5%
7 12
 
1.3%
8 6
 
0.7%
11 7
 
0.8%
ValueCountFrequency (%)
11 7
 
0.8%
8 6
 
0.7%
7 12
 
1.3%
6 22
 
2.5%
5 15
 
1.7%
4 29
 
3.3%
3 102
 
11.4%
2 161
 
18.1%
1 537
60.3%

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct891
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446
Minimum1
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2025-09-08T16:46:57.940166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.5
Q1223.5
median446
Q3668.5
95-th percentile846.5
Maximum891
Range890
Interquartile range (IQR)445

Descriptive statistics

Standard deviation257.35384
Coefficient of variation (CV)0.57702655
Kurtosis-1.2
Mean446
Median Absolute Deviation (MAD)223
Skewness0
Sum397386
Variance66231
MonotonicityStrictly increasing
2025-09-08T16:46:58.157872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
599 1
 
0.1%
588 1
 
0.1%
589 1
 
0.1%
590 1
 
0.1%
591 1
 
0.1%
592 1
 
0.1%
593 1
 
0.1%
594 1
 
0.1%
595 1
 
0.1%
Other values (881) 881
98.9%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
891 1
0.1%
890 1
0.1%
889 1
0.1%
888 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
884 1
0.1%
883 1
0.1%
882 1
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
0
549 
1
342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters891
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Length

2025-09-08T16:46:58.367469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-08T16:46:58.509739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring characters

ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 549
61.6%
1 342
38.4%

Interactions

2025-09-08T16:46:51.840199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:45.400327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.498881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:47.480640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.803731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.818603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:50.867270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.991662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:45.588688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.648897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:47.631343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.960151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.978445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.017778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:52.135797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:45.756998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.792143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.053908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.119109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:50.128967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.159231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:52.273012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:45.904627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.932318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.210613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.273960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:50.276308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.290158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:52.403755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.047873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:47.067883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.354424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.417818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:50.425476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.428468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:52.553959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.209216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:47.213220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.510460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.560023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:50.578228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.576760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:52.687422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:46.359605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:47.344148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:48.657124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:49.690572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:50.723393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-08T16:46:51.706396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-09-08T16:46:58.630593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeAge*ClassAge*FareAloneCabinEmbarkedFamilySizeFareParchPassengerIdPclassSexSibSpSurvivedTitle
Age1.0000.7290.1710.4040.1360.1360.2030.1140.2380.0000.2700.2770.2280.1920.497
Age*Class0.7291.000-0.0740.4180.2250.341-0.316-0.550-0.2920.0120.6590.451-0.2600.4030.535
Age*Fare0.171-0.0741.0000.2590.2390.1950.2120.7760.0880.0230.4560.1700.1890.2370.128
Alone0.4040.4180.2591.0000.1830.1100.9960.3040.6860.0000.1270.3000.8370.1980.492
Cabin0.1360.2250.2390.1831.0000.1780.0860.2870.0250.0000.5980.1820.0170.3200.111
Embarked0.1360.3410.1950.1100.1781.0000.1350.1950.0520.0000.2580.1110.0920.1640.132
FamilySize0.203-0.3160.2120.9960.0860.1351.0000.5290.801-0.0500.2030.3130.8490.2860.359
Fare0.114-0.5500.7760.3040.2870.1950.5291.0000.410-0.0140.4790.1890.4470.2830.120
Parch0.238-0.2920.0880.6860.0250.0520.8010.4101.0000.0010.0220.2470.4500.1570.311
PassengerId0.0000.0120.0230.0000.0000.000-0.050-0.0140.0011.0000.0320.066-0.0610.1040.054
Pclass0.2700.6590.4560.1270.5980.2580.2030.4790.0220.0321.0000.1300.1480.3370.133
Sex0.2770.4510.1700.3000.1820.1110.3130.1890.2470.0660.1301.0000.2060.5400.999
SibSp0.228-0.2600.1890.8370.0170.0920.8490.4470.450-0.0610.1480.2061.0000.1870.340
Survived0.1920.4030.2370.1980.3200.1640.2860.2830.1570.1040.3370.5400.1871.0000.571
Title0.4970.5350.1280.4920.1110.1320.3590.1200.3110.0540.1330.9990.3400.5711.000

Missing values

2025-09-08T16:46:52.884612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-08T16:46:53.187552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PclassSexAgeSibSpParchFareCabinEmbarkedTitleAge*ClassAge*FareAloneFamilySizePassengerIdSurvived
03male[20, 25)107.2500missingSMr66.0159.500000True210
11female[35, 40)1071.2833CCMrs38.02708.765400True221
23female[25, 30)007.9250missingSMiss78.0206.050000False131
31female[35, 40)1053.1000CSMrs35.01858.500000True241
43male[35, 40)008.0500missingSMr105.0281.750000False150
53male[30, 35)008.4583missingQMr98.953237278.992056False160
61male[50, 55)0051.8625ESMr54.02800.575000False170
73male[0, 5)3121.0750missingSMaster6.042.150000True580
83female[25, 30)0211.1333missingSMrs81.0300.599100True391
92female[10, 15)1030.0708missingCMrs28.0420.991200True2101
PclassSexAgeSibSpParchFareCabinEmbarkedTitleAge*ClassAge*FareAloneFamilySizePassengerIdSurvived
8813male[30, 35)007.8958missingSMr99.0260.561400False18820
8823female[20, 25)0010.5167missingSMiss66.0231.367400False18830
8832male[25, 30)0010.5000missingSMr56.0294.000000False18840
8843male[25, 30)007.0500missingSMr75.0176.250000False18850
8853female[35, 40)0529.1250missingQMrs117.01135.875000True68860
8862male[25, 30)0013.0000missingSMr54.0351.000000False18870
8871female[15, 20)0030.0000BSMiss19.0570.000000False18881
8883female[20, 25)1223.4500missingSMiss65.536913512.280201True48890
8891male[25, 30)0030.0000CCMr26.0780.000000False18901
8903male[30, 35)007.7500missingQMr96.0248.000000False18910